In this paper we report a size and position invariant
human posture recognition algorithm. The algorithm employs a
simplified line segment Hausdorff distance classification and uses
projection histograms to achieve size and position invariance.
Compared to other existing method utilizing line segment Hausdorff
distance, the proposed algorithm reduces the computation
complexity by 36000 times, for our test images. Combining bioinspired
event-based image acquisition and hardware friendly
feature extraction and classification algorithm will lead to a
promising technology for use in wireless sensor network.